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Record W2589288095 · doi:10.1111/tri.12944

Patients’ preferences in transplantation from marginal donors: results of a discrete choice experiment

2017· article· en· W2589288095 on OpenAlexaff
Sara Kamran, Filoména Conti, Marie‐Pascale Pomey, Gabriel Baron, Yvon Calmus, G. Vidal-Trécan

Bibliographic record

VenueTransplant International · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversité de Montréal
FundersAgence de la Biomédecine
KeywordsMedicineTransplantationLiver transplantationMarginal utilityWaiting listSurgeryIntensive care medicine

Abstract

fetched live from OpenAlex

To increase the donor pool, the strategy of transplantation from "marginal" donors was developed though patients' preferences about these donors were insufficiently known. The preferences of patients registered on the waiting list or already transplanted in eight transplant teams covering four main organs (i.e., kidney, liver, heart, and lung) were evaluated using the discrete choice experiment method. In each left during 2 days, patients were interviewed on four scenarios. Of 178 eligible patients, 167 were interviewed; 40% accepted marginal graft in their own situation and 89% at least in one of the scenarios. Imagining urgent situations or rare profiles with difficult access to transplantation, respectively, 86% and 71% accepted these grafts. Most (76%) preferred to be informed about these grafts and 43% preferred to be involved in decision. The emergency [OR = 1.24; 95% CI: (1.06-1.45)] and the hazardousness [OR = 0.88; 95% CI: (0.78-0.99)] of the transplantation were factors independently associated with marginal graft acceptance. Most patients preferred to be informed and to be involved in the decision. Marginal grafts could be more accepted by patients in critical medical situations or perceiving their situation as critical. Physicians' practices in transplantation should be reconsidered taking into account individual preferences. This study was performed in a single country and thus reflects the cultural bias and practice thereof.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.306
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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